In the traditional Multiple Hypothesis Tracker(MHT) algorithm, it is usually assumed that the clutter intensity is known a priori. When the clutter of observation scene is unknown and spatially variable, the performance of the tracking algorithm drops sharply. To solve this problem, an improved MHT method with clutter estimation based on adaptive Gaussian Mixture Model(GMM) is proposed. Firstly, the adaptive GMM is utilized to fit the spatial distribution of unknown clutter, and the clutter intensity in the gate is estimated adaptively. Then, it is applied to the MHT tracker to effectively improve the accuracy of track score calculation and optimal hypothetical track estimation, so as to realize stable tracking in unknown clutter scene. Simulation results show that the proposed algorithm achieves better data association accuracy and track maintenance performance than the standard MHT algorithm and the MHT-GMM algorithm in unknown clutter observation scene.